In the past few months we have all read and heard about ChatGPT everywhere, I’m going to add a little to that discussion.
Many startups and companies are in danger of becoming obsolete, with LLMs threatening to erode the value propositions that made them competitive. However, there are possible moats that could be employed to keep these businesses afloat. LLMs like ChatGPT have shown a remarkable ability to understand and generate human-like text. This has resulted in the disruption of various industries, such as customer support, content creation, among others. Consequently, many startups that once thrived by offering specialized services in these areas are now scrambling to reinvent themselves or risk fading into obsolescence.
The integration of ChatGPT plugins will streamline many daily tasks, such as ordering food, booking appointments, or even managing finances. By providing users with a single, unified interface for interacting with these services, ChatGPT is becoming a one-stop-shop for the interface to many apps and businesses. This centralization of functionality undermines the value of standalone apps and services, leaving many startups scrambling to adapt or risk becoming obsolete.
One of the most significant challenges startups face is the breakneck pace at which LLMs continue to improve. With each new iteration, these models become more accurate and versatile, making it increasingly difficult for businesses to justify their existence based on their unique offerings. What might be differentiated today could become a part of core offerings like ChatGPT tomorrow. In essence, LLMs are beginning to consume the value propositions that once distinguished many startups and companies from their competitors.
Despite these looming threats, several strategies can help startups maintain their competitive advantage in the face of advancing LLMs. By investing in these potential moats, businesses can ensure their long-term survival and success:
- Specialized domain expertise: Companies that possess deep and specialized knowledge in a particular field can differentiate themselves from LLMs. There are many features that don’t make sense for ChatGPT, but do make sense for a niche use case.
- Complementary services: Instead of competing against LLMs, startups can pivot towards providing complementary services that enhance the performance of these models. For example, companies can offer specialized data sets, fine-tuning services, or even develop custom interfaces that make LLMs more user-friendly and accessible.
In conclusion, the rapid improvement of large language models like ChatGPT poses a significant threat to many startups and companies. As these models continue to evolve, they will consume an increasing number of value propositions that once set businesses apart.
I’m an entrepreneur trying to figure out potential moats for new businesses based on these technologies. I’ve started a group on discord to connect entrepreneurs together that are worried about these technologies and want to find ways to compete.
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